def mv1pmf_skel(dataset, check_repro=True, args=None):
MIN_CONF_THRES = args.thres2d
no_img = not (args.vis_det or args.vis_repro)
dataset.no_img = no_img
kp3ds = []
start, end = args.start, min(args.end, len(dataset))
kpts_repro = None
for nf in tqdm(range(start, end), desc='triangulation'):
images, annots = dataset[nf]
check_keypoints(annots['keypoints'],
WEIGHT_DEBUFF=1,
min_conf=MIN_CONF_THRES)
keypoints3d, kpts_repro = simple_recon_person(annots['keypoints'],
dataset.Pall)
if check_repro:
keypoints3d, kpts_repro = check_repro_error(
keypoints3d,
kpts_repro,
annots['keypoints'],
P=dataset.Pall,
MAX_REPRO_ERROR=args.MAX_REPRO_ERROR)
# keypoints3d, kpts_repro = robust_triangulate(annots['keypoints'], dataset.Pall, config=config, ret_repro=True)
kp3ds.append(keypoints3d)
if args.vis_det:
dataset.vis_detections(images, annots, nf, sub_vis=args.sub_vis)
if args.vis_repro:
dataset.vis_repro(images, kpts_repro, nf=nf, sub_vis=args.sub_vis)
# smooth the skeleton
if args.smooth3d > 0:
kp3ds = smooth_skeleton(kp3ds, args.smooth3d)
for nf in tqdm(range(len(kp3ds)), desc='dump'):
dataset.write_keypoints3d(kp3ds[nf], nf + start)
def mv1pmf_smpl(dataset, args, weight_pose=None, weight_shape=None):
dataset.skel_path = args.skel
kp3ds = []
start, end = args.start, min(args.end, len(dataset))
keypoints2d, bboxes = [], []
dataset.no_img = True
for nf in tqdm(range(start, end), desc='loading'):
images, annots = dataset[nf]
keypoints2d.append(annots['keypoints'])
bboxes.append(annots['bbox'])
kp3ds = dataset.read_skeleton(start, end)
keypoints2d = np.stack(keypoints2d)
bboxes = np.stack(bboxes)
kp3ds = check_keypoints(kp3ds, 1)
# optimize the human shape
with Timer('Loading {}, {}'.format(args.model, args.gender),
not args.verbose):
body_model = load_model(gender=args.gender, model_type=args.model)
params = smpl_from_keypoints3d2d(body_model,
kp3ds,
keypoints2d,
bboxes,
dataset.Pall,
config=dataset.config,
args=args,
weight_shape=weight_shape,
weight_pose=weight_pose)
# write out the results
dataset.no_img = not (args.vis_smpl or args.vis_repro)
for nf in tqdm(range(start, end), desc='render'):
images, annots = dataset[nf]
param = select_nf(params, nf - start)
dataset.write_smpl(param, nf)
if args.vis_smpl:
vertices = body_model(return_verts=True,
return_tensor=False,
**param)
dataset.vis_smpl(vertices=vertices[0],
faces=body_model.faces,
images=images,
nf=nf,
sub_vis=args.sub_vis,
add_back=True)
if args.vis_repro:
keypoints = body_model(return_verts=False,
return_tensor=False,
**param)[0]
kpts_repro = projectN3(keypoints, dataset.Pall)
dataset.vis_repro(images, kpts_repro, nf=nf, sub_vis=args.sub_vis)